6 Best Programming Languages for Machine Learning in 2022

Machine learning is a fantastic example of an invention that has lately moved from academic research and theoretical studies to practical implementations and continuous assistance for commercial operations. 

 

The most difficult element of learning machine learning if you're new to the domain is identifying where to start. It's reasonable to question which language is ideal for pursuing a successful career in the field of machine learning, whether you're new to machine learning or want to brush up on your abilities.

 

( Related - Types of Machine Learning Methods )

 

Let us first get a gist of what machine learning is. 

 

What is Machine Learning?

 

See, Machine learning is a branch of artificial intelligence (AI) that allows computers to learn and develop on their own without having to be specifically programmed. It is concerned with the creation of computer programs that can access data and information on their own and train for themselves. 

 

The learning process starts with observations or data, such as examples, actual experience, or instruction, so that we may seek patterns in data and make better judgments in the future based on the examples we offer. The fundamental goal is for computers to learn on their own, without the need for human involvement, and to adapt their behavior accordingly.

 

Now that we’ve offered an idea on what Machine Learning is, this blog is going to explore the best programming languages for learning and working in the field of Machine Learning.

 

It's arguable how much programming language expertise is required to build machine learning models effectively. It is entirely dependent on the sort of application and the type of real-world challenges being addressed. To get the most out of machine learning, you'll need a fundamental understanding of programming languages and their key features, such as algorithms, data structures, logic, and memory management.

 

Some of the best programming languages which you can use if you are interested in exploring Machine Learning are given below -

 

1. Python 

 

Inarguably the easiest programming language ever developed. Python is a lightweight, flexible, and easy programming language that, when combined with the right framework, can power sophisticated scripting and online applications.

 

It has long been regarded by developers as a basic, easy-to-learn language, and its popularity knows no boundaries. It is flexible since it supports different frameworks and libraries. Python is popular among programmers because it provides a lot of flexibility. 

 

It contains numerous visualization packages and essential core libraries like sklearn, seaborn, and others thanks to its scalability and open-source nature. These sophisticated libraries make coding simple and give machines the ability to learn more. Python is the primary programming language used for machine learning at major companies like Google, Instagram, Facebook, Dropbox, Netflix, Walt Disney, YouTube, Uber, Amazon, and Reddit, among other IT behemoths. 

 

( Related blog - How Netflix maximises customer experience )

 

Python's multi-paradigm and dynamic structure allows machine learning professionals to tackle a problem in the most straightforward way feasible. It supports procedural, functional, object-oriented, and imperative programming styles, allowing machine learning specialists to focus on the technique that best suits their needs.

 

There are several Python libraries for AI, deep learning, artificial intelligence, natural language processing, and so on, such as Teano, Keras, and scikit-discover. For example, Numpy is a library that helps with a variety of calculations, while Pybrain is a Python library for machine learning.

 

 

2. R Programming Language 

 

R programming language is a prominent open-source data visualization-driven language with a strong focus on statistical computation and a strong presence in the machine learning world. 

 

The R Foundation and the R development core team are in charge of it. R has a huge resource pool, due to its key features that help in the development of machine learning applications. It has a wide range of applications in data and statistics. With its powerful computational capabilities, it can provide effective machine learning solutions.

 

Understanding statistical concepts so that they can apply those principles to huge data is an important component of a machine learning engineer's day-to-day work responsibilities. And as we know when it comes to crunching huge numbers, the R programming language is a brilliant choice, and hence the language of choice for machine learning applications that require a lot of statistical data.

---

Some of the Top Programming languages for Machine Learning

---

3. JAVA 

 

JavaScript and Java are multifunctional programming languages that have shown to be useful in machine learning techniques and applications. These object-oriented languages are known for their consistency and dependability, as well as their capacity to handle large amounts of data.

 

Machine learning algorithms are supported by Java frameworks such as Weka, Rapid Miner, and others.  Java is a very user-friendly programming language yet a bit of a complex language that allows for simple debugging, data visualization, big package services, improved user engagement, and job simplification in large projects.

 

For a variety of reasons, the language itself is widely criticized. But let's go right to the point: the language is complicated, with numerous regulations in place, which makes writing for Java developers much slower. You will encounter issues if you stay in Java, but if you copy-paste your problem into Google, you will find tons of solutions to assist you.

 

TensorFlow.js is a Google-created open-source toolkit that utilizes JavaScript to build machine learning models in the browser or JavaScript in Node.js. For individuals who aren't much experienced with web programming, TensorFlow.js is a wonderful way to get started with machine learning.

 

Because TensorFlow.js supports WebGL, your machine learning models can run even when a GPU isn't available. Java and JavaScript are supported by a number of machine learning packages and frameworks.

 

4. Julia 

 

Julia is a popular high-level, dynamic programming language designed specifically for constructing machine learning applications. It is a favored pick for developers since it is a high-performance language with a simple syntax. Julia is a true underdog: while not being as well-known as Python and R, it was designed to combine the capabilities of Python, MATLAB, and R with the pace of C++ and Java. 

 

Julia specializes in scientific computing and is well-suited to it. Julia is scalable and quicker than Python and R due to these computational capabilities. Julia is especially well-suited to implement the fundamental math and scientific questions that underpin the majority of machine learning algorithms. 

 

The LLVM framework is used to build Julia programs just in time or at run time. This allows machine learning developers to work quickly without relying on manual profiling or optimization approaches to solve all of the performance issues.

 

Julia is powering machine learning applications at large firms like Apple, Disney, Oracle, and NAS, with support for all sorts of hardware including TPUs and GPUs on any cloud. 

 

( Related - Julia Vs Python )

 

5. C++

 

C++ is a strong, adaptable, and widely used programming language that has been chosen by many people all around the world. This is still true when it comes to building machine learning algorithms. C and C++ are specialized in terms of machine learning being one of the oldest programming languages. 

 

C may be used to supplement current machine learning projects, and computer hardware experts favor C because of its speed and control. And the most fun part is that you can write algorithms from scratch with C/C++. Because of its ML repositories like TensorFlow, LightGBM, and Turi Create, C++ is an excellent choice for Machine Learning. 

 

The two most important aspects of C++ are speed and efficacy. In this approach, when properly implemented, C++ may aid in the development of fast and well-coded algorithms. Developers may use the programming language to create a powerful machine learning and computer vision programs. C++ also comes with a number of fundamental features, such as a memory management system and others.

 

( Also Read - 6 Latest Programming Languages )

 

 

6. Scala

 

Scala is a well-known compiled language that allows you to write quick executable programs. Scala is far quicker than Python because it combines the finest features of object-oriented and functional programming into a single high-level language. 

 

According to Neptune.ai, It was designed specifically for the Java Virtual Machine (JVM) and makes interacting with Java code a breeze. It features a powerful backend language and can thus handle a large amount of data. Scala is a preferred choice for Big Data and Data Science thanks to libraries like Apache Spark. 

 

By combining Spark capabilities with other big data tools and technologies, it provides developers with an efficient approach for building, implementing, and deploying machine learning algorithms.

 

( Interested in Machine Learning? Here is a list of the best Machine Learning Courses )

 

 

Conclusion

 

Machine learning is a rapidly developing field of computer science, and ML frameworks and libraries are supported by a variety of programming languages. So, in the year 2021, what is the greatest language for Machine Learning? This is the one that best meets your requirements. You're not sure what you desire right now? So first figure out what your final aim is, what you want to create, and then select the finest programming language based on that.